The wireless sensor networks (WSNs) consists of a number of miniature low-power sensor nodes. The sensor nodes are chiefly equipped with several micro-sensors, a microprocessor, and a radio chip providing wireless communication capability. The functionalities of sensor nodes form WSNs due to their wide and valuable applicability in various fields. Applications of WSNs also stimulated great interests in developing wireless ad-hoc sensor networks. Unlike existing hardwired networks, the logical topology of a sensor net-work is not necessarily associated with its physical topology. Usually, a sensor network is data-centric system that measures the sensing events according to the attributes of the events. The data sensed by sensor networks are meaningless if we do not know the locations where the sensing events are occurred. Thus, to provide a reliable localization scheme is a fundamental but essential issue for the applications of WSNs when the location information of sensor nodes is required.
There are two easy ways to determine the location of each sensor node. The location information may be obtained while the network was deployed manually. The other approach is to equip each sensor node with a self-positioning device, e.g., global positioning system (GPS). However, these methods are unrealistic to deploy a large-scale sensor network. Recently, many localization algorithms for WSNs have been proposed. These algorithms can be categorized either as range-free or range-aware algorithms based on whether they use the range (i.e., distance) information.
The range-aware approaches measure the distance between two sensor nodes based on physical measurements. Existing localization methods make use of three types of physical measurements: time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS) or energy. These methods are mainly based on the measurements of acoustic ultrasounds or electromagnetic signals transmitted between sensor nodes. These approaches are found to have their own advantages and disadvantages. Ultrasounds-based TOA and TDOA estimations are not suitable for many practical applications due to signal-reverberating effects. A number of environmental factors may shorten the range of ultrasound propagation, e.g. scattering, absorption, and reflection when ultrasound wave encounters a small particle compared to its wavelength. These drawbacks make the ultrasound-based approaches unreliable. Radio-based TOA and TDOA estimations require high synchronization accuracy up to nanosecond for correct operation. In addition, measurement of AOA requires a set of carefully calibrated directional antennas, which significantly increases the cost and system complexity.
Due to the drawback of range-aware approaches, a number of range-free localization methods have been proposed, such as centroid, area-based point-in-triangulation, ad-hoc positioning system, convex position estimation, distributed localization estimation, Monte Carlo localization, and mobile and static sensor network localization. The error rates of range-free algorithms are high if the communication range of sensor nodes is not circular. In addition, the range-free algorithms require several sensor nodes working together to accomplish a localization task, so that they suffer from power consuming. Among the approaches mentioned above, the radio propagation model is known as a simple function under a priori assumption. Such an assumption is an over-simplification for many scenarios.
To address these challenges, a localization framework for WSNs without adding expensive hardware (e.g., GPS, time synchronizer, sensitive timer) to the sensor nodes is proposed. The basic principle of the proposed framework is to make use of the phenomenon of radio irregularity in WSNs. In addition, a robust correlation is incorporated in analyzing the relative positions between two sensor nodes using received signal strength indication (RSSI) pattern. A cooperative localization scheme is also developed to reinforce the accuracy of the estimation while multiple fixed sensor nodes are available.
It is therefore attempted by the applicant to deal with the above situation encountered in the prior art.